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<table width="100%" summary="page for muscatine"><tr><td>muscatine</td><td style="text-align: right;">R Documentation</td></tr></table>

<h2>Data on Obesity from the Muscatine Coronary Risk Factor Study.</h2>

<h3>Description</h3>

<p>The data are from the Muscatine Coronary Risk Factor (MCRF) study,
a longitudinal survey of school-age children in Muscatine, Iowa.
The MCRF study had the goal of examining the development and
persistence of risk factors for coronary disease in children.  In
the MCRF study, weight and height measurements of five cohorts of
children, initially aged 5-7, 7-9, 9-11, 11-13, and 13-15 years,
were obtained biennially from 1977 to 1981. Data were collected on
4856 boys and girls. On the basis of a comparison of their weight
to age-gender specific norms, children were classified as obese or
not obese.
</p>


<h3>Usage</h3>

<pre>
muscatine
</pre>


<h3>Format</h3>

<p>A dataframe with 14568 rows and 7 variables:
</p>

<dl>
<dt>id</dt><dd><p>identifier of child.</p>
</dd>
<dt>gender</dt><dd><p>gender of child</p>
</dd>
<dt>base_age</dt><dd><p>baseline age</p>
</dd>
<dt>age</dt><dd><p>current age</p>
</dd>
<dt>occasion</dt><dd><p>identifier of occasion of recording</p>
</dd>
<dt>obese</dt><dd><p>'yes' or 'no'</p>
</dd>
<dt>numobese</dt><dd><p>obese in numerical form: 1 corresponds to 'yes'
and 0 corresponds to 'no'.</p>
</dd>
</dl>


<h3>Source</h3>

<p><a href="https://content.sph.harvard.edu/fitzmaur/ala2e/muscatine.txt">https://content.sph.harvard.edu/fitzmaur/ala2e/muscatine.txt</a>
</p>
<p>Woolson, R.F. and Clarke, W.R. (1984). Analysis of categorical
incompletel longitudinal data. Journal of the Royal Statistical Society,
Series A, 147, 87-99.
</p>


<h3>Examples</h3>

<pre>

                                                                        
muscatine$cage &lt;- muscatine$age - 12                                         
muscatine$cage2 &lt;- muscatine$cage^2                                          
                                                                        
f1 &lt;- numobese ~ gender                                                 
f2 &lt;- numobese ~ gender + cage + cage2 +                                
    gender:cage + gender:cage2                                          
                                                                        
gee1 &lt;- geeglm(formula = f1, id = id,                                   
               waves = occasion, data = muscatine, family = binomial(),      
               corstr = "independence")                                 
                                                                        
gee2 &lt;- geeglm(formula = f2, id = id,                                   
               waves = occasion, data = muscatine, family = binomial(),      
               corstr = "independence")                                 
                                                                        
tidy(gee1)                                                              
tidy(gee2)                                                              
QIC(gee1)
QIC(gee2)


</pre>


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